Applications · Metabolomics
Metabolomics with clearer mass features
Turn complex untargeted LC/MS datasets into cleaner, aligned, and quantified mass feature results.
The challenge
Signal that hides in the noise
Untargeted metabolomics generates large, noisy LC/MS datasets. Real mass features can be missed, split, or buried, and legacy workflows may recover only a subset of what is detectable.
Metablify amplifies consistent signal and suppresses noise, so more of the true metabolome is recovered from the same experiment.
Workflow
From untargeted data to quantified features
Three stages carry your metabolomics data from raw signal to results ready for analysis.
01
Detect
Surface real mass features buried in background signal across untargeted runs.
02
Align
Match features across large, noisy sample cohorts so groups stay comparable.
03
Quantify
Produce cleaner outputs ready for statistics and downstream discovery.
Outcomes
Stronger foundations for downstream discovery
Cleaner, higher confidence mass feature data reduces manual review and provides a stronger foundation for metabolomics workflows that depend on accurate detection, alignment, and quantification.
Detect real mass features buried in background signal
Align features across large, noisy sample cohorts
Quantify with outputs ready for downstream analysis
Where it fits
Built for real studies
Untargeted discovery
Cast a wide net across the metabolome and recover more of what is really there.
Large cohort studies
Keep alignment and quantification stable as sample counts grow into the hundreds.
Biomarker candidates
Build a cleaner foundation for the features that matter to your hypothesis.
Ready to apply Metablify to metabolomics?
Bring us your samples, LC/MS data, or workflow challenge.
Discuss a Project